This page presents Eric Jordan's paper on how artificial intelligence should be used in business valuation: as a research and organizational tool that supports, but does not replace, verified data, structured methodology, direct observation, and experienced professional judgment.
Artificial Intelligence as a Research Tool in Business Valuation
1. Introduction
Artificial intelligence has rapidly become a commonly used tool in research, data gathering, and analysis across many industries, including business valuation. Systems such as ChatGPT, Gemini, Grok, and Perplexity are capable of processing large volumes of information and generating structured outputs in a matter of seconds.
While these systems provide significant efficiencies in information discovery, they are not determinative of truth. Artificial intelligence systems operate through probabilistic pattern recognition and are dependent on the quality, scope, and structure of the data on which they are trained.
Accordingly, the use of artificial intelligence in business valuation must be understood as assisting the research process, rather than replacing professional judgment. This paper focuses specifically on the valuation of privately held businesses, where the identification and interpretation of intangible assets are often significantly more complex than in publicly traded companies.
2. Professional Background and Context
The author is a Canadian Personal Property Appraiser (CPPA) and has been self-employed since the age of eighteen, with over four decades of direct business ownership experience involving personal financial exposure and responsibility.
Since 1998, the author has worked extensively with internet-based search systems, beginning with early search engines and continuing through the evolution of machine-learning-driven search and modern artificial intelligence tools.
In addition, the author has completed training in artificial intelligence fundamentals through the University of Helsinki (Elements of AI) program. This training was also completed by a team member.
The author is also the developer of the 25 Factors Affecting Business Valuation methodology and the 5 Senses Inspection Report. These methodologies are designed specifically for the valuation of privately held businesses, with a primary focus on identifying, measuring, and weighting intangible assets.
3. Nature and Limitations of Artificial Intelligence
Artificial intelligence systems, including large language models, do not "understand" information in a human sense. They generate outputs based on statistical patterns identified within large datasets.
As a result, such systems may:
- Produce plausible but incorrect information
- Reflect bias present in training data
- Present outdated or incomplete information
- Express outputs with unwarranted confidence
For this reason, artificial intelligence must be used with caution. It is most appropriately applied as a research assistant, with all outputs subject to independent verification and professional evaluation.
4. Artificial Intelligence as a Research Tool
Within the context of business valuation, artificial intelligence can be used effectively for:
- Identifying industry data and trends
- Locating comparable businesses or transactions
- Generating preliminary analytical frameworks
- Organizing large volumes of information
However, the final determination of value must be based on verified data, real-world operational understanding, structured valuation methodology, and professional judgment. This distinction is particularly important in the valuation of privately held businesses, where publicly available data is often limited and incomplete.
5. The Role of Experience and Pattern Recognition
The identification and measurement of intangible assets in privately held businesses require a form of judgment that develops through long-term practical experience. This judgment is best described as pattern recognition developed through repeated real-world exposure under conditions of financial responsibility and risk.
This form of expertise is well recognized across multiple disciplines:
- In professional dance, experienced practitioners develop the ability to perform seamlessly with unfamiliar partners through years of practice and internalized pattern recognition.
- In Olympic-level figure skating, athletes typically train for a decade or more to develop coordination, awareness, and control that cannot be acquired through theoretical instruction.
- In aviation, experienced pilots develop the ability to recognize abnormal conditions and respond appropriately based on accumulated flight experience.
- In surgical practice, experienced surgeons often detect complications through subtle cues developed over years of hands-on work.
In each of these disciplines, performance depends on tacit knowledge that cannot be acquired through study alone. Similarly, in privately held business valuation, the recognition of intangible asset patterns depends on long-term exposure to real operating businesses where outcomes have direct financial consequences.
6. Experience Requirement for Application of the Methodology
The Eric Jordan 25 Factors Affecting Business Valuation methodology requires a practitioner to identify, measure, and weigh both tangible and intangible assets that influence fair market value in privately held businesses.
For this reason, a practitioner must possess a minimum of ten to fifteen years of relevant hands-on experience as a business owner with direct personal financial exposure to the success or failure of the enterprise. This requirement reflects the necessity of having "skin in the game," where the practitioner has been accountable for real financial outcomes.
Managerial roles without ownership risk, passive ownership, or inherited operations without meaningful entrepreneurial responsibility do not meet this requirement. Without this level of experience, the practitioner may be unable to properly recognize, interpret, and weigh the intangible assets that frequently represent the majority of value in privately held businesses.
7. Limitations of Traditional Valuation Approaches
The market, asset, and income approaches are widely accepted frameworks in business valuation and provide important reference points. However, in the context of privately held businesses, these approaches may be incomplete when intangible assets are not fully identified, measured, and weighted.
Unlike publicly traded companies, where market data is more readily available and pricing is continuously tested, privately held businesses often derive a significant portion of their value from intangible factors that are not directly observable. In many privately held businesses, intangible assets can represent up to 90 percent or more of total enterprise value, particularly in service-based, relationship-driven, and owner-operated enterprises.
Traditional approaches typically rely on historical financial data, comparable transactions, and balance sheet asset values. While these inputs are important, they may not fully reflect:
- Customer relationships and loyalty
- Operational systems and efficiencies
- Brand strength and market position
- Management capability
- Workforce effectiveness
- Supply chain stability
As a result, valuations based solely on these approaches may arrive at conclusions that appear reasonable, while not fully identifying or explaining the underlying drivers of value.
8. Integration of Structured Methodology
To address these limitations, the Eric Jordan 25 Factors Affecting Business Valuation methodology provides a structured framework for identifying and evaluating both tangible and intangible assets in privately held businesses.
This methodology is supported by the 5 Senses Inspection Report, which incorporates direct observation of business operations through sight, sound, smell, touch and feel, and, where applicable, taste. This approach captures operational realities, environmental conditions, and experiential indicators of value that are not reflected in financial statements or purely quantitative models.
9. Role of Artificial Intelligence within the Methodology
Artificial intelligence is used within this framework as a supporting research tool. Specifically, it is used to:
- Identify relevant data sources
- Assist in organizing information
- Generate preliminary analytical perspectives
All information obtained through artificial intelligence systems is independently verified through external sources and professional evaluation. Final conclusions regarding value are determined through application of the 25 Factors methodology, direct observation through the 5 Senses Inspection Report, and professional judgment developed through long-term experience. Artificial intelligence does not determine value. It supports the research process.
10. Professional and Evidentiary Considerations
In the context of expert analysis involving privately held businesses, the reliability of conclusions depends on the qualifications and experience of the practitioner, the transparency of the methodology applied, the ability to explain how conclusions are reached, and the verification of underlying information.
The approach described in this paper is grounded in:
- Long-term business ownership experience involving financial risk
- Structured methodology for identifying and measuring intangible assets
- Responsible use of artificial intelligence as a research tool
- Independent verification of all relevant data
These elements collectively support a reasoned and defensible determination of fair market value.
11. Conclusion
Artificial intelligence represents a valuable advancement in research capability, but it must be used with a clear understanding of its limitations. The accurate valuation of privately held businesses requires more than data analysis. It requires the identification and interpretation of intangible assets that often represent the majority of enterprise value.
This process depends on structured methodology, verified information, direct observation, and long-term experiential judgment. Practitioners who possess ten to fifteen years or more of relevant hands-on business ownership experience develop the pattern recognition necessary to apply these principles effectively.
Artificial intelligence enhances the research process. However, the determination of fair market value remains grounded in professional judgment, real-world experience, and the disciplined application of methodology.
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